Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- Open-Orca/OpenOrca
+- OpenAssistant/oasst_top1_2023-08-25
+language:
+- bg
+- ca
+- cs
+- da
+- de
+- en
+- es
+- fr
+- hr
+- hu
+- it
+- nl
+- pl
+- pt
+- ro
+- ru
+- sl
+- sr
+- sv
+- uk
+
+library_name: transformers
+---
+
+```
+reference-data-model:
+
+  datasets:
+    - OpenAssistant/oasst_top1_2023-08-25:
+      lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk"
+      link: https://huggingface.co/datasets/OpenAssistant/oasst_top1_2023-08-25
+
+  model:
+    - Open-Orca/Mistral-7B-OpenOrca
+      Link:
+        https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca
+
+  100 examples of generating:
+    - Link:
+      https://huggingface.co/NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v3/blob/main/output.xlsx
+
+  Activated training with:
+    - Link:
+        https://huggingface.co/blog/tomaarsen/attention-sinks
+        https://github.com/tomaarsen/attention_sinks
+        https://arxiv.org/abs/2309.17453
+
+  Version 1:
+    - Link:
+        https://huggingface.co/NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1
+
+```
+
+
+## 
+
+
+```py
+# attention-sinks
+pip install attention_sinks
+
+# flash-attn
+!export CUDA_HOME=/usr/local/cuda-11.8
+!MAX_JOBS=4 pip install flash-attn --no-build-isolation -qqq
+!pip install git+"https://github.com/HazyResearch/flash-attention.git#subdirectory=csrc/rotary" -qqq
+```
+
+
+## Version
+```py
+import torch, transformers,torchvision
+torch.__version__,transformers.__version__, torchvision.__version__
+#OUTPUTS: ('2.0.1+cu118', '4.34.0', '0.15.2+cu118')
+```
+
+## How to use
+```py
+
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+    HfArgumentParser,
+    TrainingArguments,
+    pipeline,
+    logging,
+    GenerationConfig,
+    TextIteratorStreamer,
+)
+
+from attention_sinks import AutoModelForCausalLM
+
+import torch
+
+# model_id = 'Open-Orca/Mistral-7B-OpenOrca'
+model_id='NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v3'
+
+model = AutoModelForCausalLM.from_pretrained(model_id,
+                                             device_map="auto",
+                                             trust_remote_code=True,
+                                             torch_dtype=torch.bfloat16,
+                                             load_in_4bit=True,
+                                             low_cpu_mem_usage= True,
+
+                                             attention_sink_size=4,
+                                             attention_sink_window_size=1024, #512, # <- Low for the sake of faster generation
+                                             )
+
+max_length=2048
+print("max_length",max_length)
+
+
+tokenizer = AutoTokenizer.from_pretrained(model_id,
+                                          # use_fast = False,
+                                          max_length=max_length,)
+
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = 'right'
+
+#EXAMPLE #1
+txt="""<|im_start|>user
+I'm looking for an efficient Python script to output prime numbers. Can you help me out? I'm interested in a script that can handle large numbers and output them quickly. Also, it would be great if the script could take a range of numbers as input and output all the prime numbers within that range. Can you generate a script that fits these requirements? Thanks!<|im_end|>
+<|im_start|>assistant
+"""
+
+#EXAMPLE #2
+txt="""<|im_start|>user
+Estoy desarrollando una REST API con Nodejs, y estoy tratando de aplicar algún sistema de seguridad, ya sea con tokens o algo similar, me puedes ayudar?<|im_end|>
+<|im_start|>assistant
+"""
+
+inputs = tokenizer.encode(txt, return_tensors="pt").to("cuda")
+
+generation_config = GenerationConfig(
+              max_new_tokens=max_new_tokens,
+              temperature=0.7,
+              top_p=0.9,
+              top_k=len_tokens,
+              repetition_penalty=1.11, 
+              do_sample=True,
+              #  pad_token_id=tokenizer.eos_token_id,
+              #  eos_token_id=tokenizer.eos_token_id,
+              #  use_cache=True,
+              # stopping_criteria= StoppingCriteriaList([stopping_criteria]),
+          )
+outputs = model.generate(generation_config=generation_config,
+                                input_ids=inputs,)
+return tokenizer.decode(outputs[0], skip_special_tokens=False) #True
+```